Elements of Research

Theoretical Framework

A theoretical framework is a collection of interrelated concepts, like a theory but not
necessarily so well worked-out. A theoretical framework guides your research, determining
what things you will measure, and what statistical relationships you will look for.

Theoretical frameworks are obviously critical in deductive, theory-testing sorts of
studies (see Kinds of Research for more information).
In those kinds of studies, the theoretical framework must be very specific and
well-thought out.

Surprisingly, theoretical frameworks are also important in exploratory studies, where
you really don't know much about what is going on, and are trying to learn more. There are
two reasons why theoretical frameworks are important here. First, no matter how little you
think you know about a topic, and how unbiased you think you are, it is impossible for a
human being not to have preconceived notions, even if they are of a very general nature.
For example, some people fundamentally believe that people are basically lazy and
untrustworthy, and you have keep your wits about you to avoid being conned. These
fundamental beliefs about human nature affect how you look things when doing personnel
research. In this sense, you are always being guided by a theoretical framework, but you
don't know it. Not knowing what your real framework is can be a problem. The framework
tends to guide what you notice in an organization, and what you don't notice. In other
words, you don't even notice things that don't fit your framework! We can never completely
get around this problem, but we can reduce the problem considerably by simply making our
implicit framework explicit. Once it is explicit, we can deliberately consider other
frameworks, and try to see the organizational situation through different lenses.

Cases and Variables

Cases are objects whose behavior or characteristics we study. Usually,
the cases are persons. But they can also be groups, departments, organizations, etc. They
can also be more esoteric things like events (e.g., meetings), utterances, pairs of
people, etc.

Variables are characteristics of cases. They are attributes. Qualities
of the cases that we measure or record. For example, if the cases are persons, the
variables could be sex, age, height, weight, feeling of empowerment, math ability, etc.
Variables are called what they are because it is assumed that the cases will vary in their
scores on these attributes. For example, if the variable is age, we obviously recognize
that people can be different ages. Of course, sometimes, for a given sample of people,
there might not be any variation on some attribute. For example, the variable 'number of
children' might be zero for all members of this class. It's still a variable, though,
because in principle it could have variation.

In any particular study, variables can play different roles. Two key roles are independent
variables and dependent variables. Usually there is only one dependent
variable, and it is the outcome variable, the one you are trying to predict. Variation in
the dependent variable is what you are trying to explain. For example, if we do a study to
determine why some people are more satisfied in their jobs than others, job satisfaction
is the dependent variable.

The independent variables, also known as the predictor or explanatory variables, are
the factors that you think explain variation in the dependent variable. In other words,
these are the causes. For example, you may think that people are more satisfied with their
jobs if they are given a lot of freedom to do what they want, and if they are well-paid.
So 'job freedom' and 'salary' are the independent variables, and 'job satisfaction' is the
dependent variable. This is diagrammed as follows:

(yes, I know. It looks like the Enterprise)

There are actually two other kinds of variables, which are basically independent
variables, but work a little differently. These are moderator and intervening
variables. A moderator variable is one that modifies the relationship between two other
variables.

For example, suppose that the cases are whole organizations, and you believe that
diversity in the organization can help make them more profitable (because diversity leads
to fresh outlooks on old problems), but only if managers are specially trained in
diversity management (otherwise all that diversity causes conflicts and miscommunication).
Here, diversity is clearly an independent variable, and profitability is clearly a
dependent variable. But what is diversity training? Its main function seems to be adjust
the strength of relation between diversity and profitability

For example, suppose you are studying job applications to various departments within a
large organization. You believe that in overall, women applicants are more likely to get
the job than men applicants, but that this varies by the number of women already in the
department the person applied to. Specifically, departments that already have a lot of
women will favor female applicants, while departments with few women will favor male
applicants. We can diagram this as follows:

Actually, if that model is true, then this one is as well, though it's harder to think
about:

Whether sex of applicant is the independent and % women in dept is the moderator, or
the other around, is not something we can ever decide. Another way to talk about
moderating and independent variables is in terms of interaction. Interacting
variables affect the dependent variable only when both are acting in concert. We could
diagram that this way:

An intervening or intermediary variable is one that is affected by the independent
variable and in turn affects the dependent variable. For example, we said that diversity
is good for profitability because diversity leads to innovation (fresh looks) which in
turn leads to profitability. Here, innovation is an intervening variable. We diagram it
this way:

Note that in the diagram, there is no arrow from diversity directly to profitability.
This means that if we control for innovativeness, diversity is unrelated to
profitability. To control for a variable means to hold its values constant. For example,
suppose we measure the diversity, innovativeness and profitability of a several thousand
companies. If we look at the relationship between diversity and profitability, we might
find that the more diverse companies have, on average, higher profitability than the less
diverse companies. But suppose we divide the sample into two groups: innovative companies
and non-innovative. Now, within just the innovative group, we again look at the
relationship between diversity and profitability. We might find that there is no
relationship. Similarly, if we just look at the non-innovative group, we might find no
relationship between diversity and profitability there either. That's because the only
reason diversity affects profitability is because diversity tends to affect a company's
innovativeness, and that in turn affects profitability.

Here's another example. Consider the relationship between education and health. In
general, the more a educated a person is, the healthier they are. Do diplomas have magic
powers? Do the cells in educated people's bodies know how to fight cancer? I doubt it. It
might be because educated people are more likely to eat nutritionally sensible food and
this in turn contributes to their health. But of course, there are many reasons why you
might eat nutritionally sensible food, even if you are not educated. So if we were to look
at the relationship between education and health among only people who eat nutritionally
sensible food, we might find no relationship. That would support the idea that nutrition
is an intervening variable.

It should be noted, however, if you control for a variable, and the relationship
between two variables disappears, that doesn't necessarily mean that the variable you
controlled for was an intervening variable. Here is an example. Look at the relationship
between the amount of ice cream sold on a given day, and the number of drownings on those
days. This is not hypothetical: this is real. There is a strong correlation: the more you
sell, the more people drown. What's going on? Are people forgetting the 'no swimming
within an hour of eating' rule? Ice cream screws up your coordination? No. There is a
third variable that is causing both ice cream sales and drownings. The variable is
temperature. On hot days, people are more likely to buy ice cream. They are also more
likely to go to the beach, where a certain proportion will drown. If we control for
temperature (i.e., we only consider days that are cold, or days that are warm), we find
that there is no relationship between ice cream sales and drownings. But temperature is
not an intervening variable, since it ice cream sales do not cause temperature
changes. Nor is ice cream sales an intervening variable, since ice cream sales do not
cause drownings.